Methods for assessing conditioning of a total loss vehicle and devices thereof

ABSTRACT

A method, non-transitory computer readable medium, and apparatus that automated assessment of conditioning includes automatically analyzing one or more electronic images of a total loss property based on one or more prior condition assessments and condition guidelines rating data associated with the total loss property. A prior property conditioning of the total loss property is determined based on the analysis of the one or more obtained images. The determined prior property conditioning of the total loss property is provided.

This application claims the benefit of U.S. Provisional PatentApplication Ser. No. 62/702,760, filed Jul. 24, 2018, which is herebyincorporated by reference in its entirety.

FIELD

This technology generally relates to methods, non-transitory computerreadable medium, and devices for assessing conditioning of a total lossproperty, such as a vehicle.

BACKGROUND

Conditioning is a measure of wear and tear attributed to an overallstate of a vehicle or other property prior to the loss. For example, ifa vehicle has received routine and scheduled maintenance in accordancewith manufacturer specifications, then both the exterior and interior ofthe vehicle will show visible signs of care that reflect a favorableconditioning. Likewise, if a vehicle has not received routine andscheduled maintenance in accordance with manufacturer specifications,then the exterior and/or interior show(s) will show visible signs ofabuse that reflect a less favorable condition.

When an electronic claim is being processed for a total loss of avehicle, the pre-loss conditioning of the vehicle is an important factorin establishing a fair market value on the total loss vehicle.Currently, existing appraisal software still requires an onsite visualvehicle damage inspection of the loss vehicle based on descriptionswithin condition guidelines and then manual input of one or morecondition ratings that subjectively best meet the prior conditioning ofthe total loss vehicle. Accordingly, these prior software assessmentsystems are time consuming, inconsistent, and subjective resulting inerrors in the establishment of the fair market value for the total lossvehicle.

SUMMARY

A method for automated assessment of conditioning includes automaticallyanalyzing, by a computing apparatus, one or more electronic images of atotal loss property based on one or more prior condition assessments andcondition guidelines rating data associated with the total lossproperty. A prior property conditioning of the total loss property isdetermined, by the computing apparatus, based on the analysis of the oneor more obtained images. The determined prior property conditioning ofthe total loss property is provided by the computing apparatus.

A non-transitory computer readable medium having stored thereoninstructions for automated assessment of conditioning comprisingexecutable code which when executed by one or more processors, causesthe one or more processors to automatically analyze one or moreelectronic images of a total loss property based on one or more priorcondition assessments and condition guidelines rating data associatedwith the total loss property. A prior property conditioning of the totalloss property is determined based on the analysis of the one or moreobtained images. The determined prior property conditioning of the totalloss property is provided.

A computing apparatus includes a memory coupled to a processor which isconfigured to be capable of executing programmed instructions stored inthe memory to automatically analyze one or more electronic images of atotal loss property based on one or more prior condition assessments andcondition guidelines rating data associated with the total lossproperty. A prior property conditioning of the total loss property isdetermined based on the analysis of the one or more obtained images. Thedetermined prior property conditioning of the total loss property isprovided.

This technology provides a number of advantages including providingmethods, non-transitory computer readable medium, and devices forassessing conditioning of a total loss vehicle or other property. Withthis technology, condition assessment artificial intelligence (AI) hasbeen developed and trained to analyze one or more electronic imagesand/or videos in conjunction with pre-established condition guidelinesrating data to automatically assess a prior property condition of atotal loss vehicle or other property. In examples of this technology,this assessment AI is executed on the total loss vehicle as a wholebased on the images without requiring disassembly of the vehicle. Thistechnology with the assessment AI provides technological improvementsresulting in increases in accuracy and consistency when evaluating aprior conditioning of the vehicle or other property. Further, with theseimprovements in the automated evaluation process there is an increase insettlement efficiency of electronic insurance claims relating to a totalloss vehicle or other property by eliminating the need for a subjectiveonsite inspection of the vehicle or other property.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an environment with an example of acondition management computing apparatus that assesses prior propertyconditioning of a total loss property;

FIG. 2 is a block diagram of the example of the condition managementcomputing apparatus shown in FIG. 1;

FIG. 3 is a flow chart of an example of a method for assessment of priorproperty conditioning of the total loss property;

FIG. 4 is a diagram of an example of identified parts of an interior ofa total loss vehicle to be assessed for prior conditioning;

FIG. 5 is a diagram of an example of identified parts of an exterior ofthe total loss vehicle to be assessed for prior conditioning;

FIG. 6 is a diagram of an example of identified parts of mechanicalelements of the total loss vehicle to be assessed for priorconditioning;

FIG. 7 is an image of an example of a tire of the total loss vehicle tobe assessed for prior conditioning;

FIG. 8 are images of an example of a parts of an interior and anexterior of the total loss vehicle; and

FIG. 9 is table of an example of an assessment of prior partconditioning of parts of a vehicle with exemplary individual and overallassessment ratings.

DETAILED DESCRIPTION

An environment 10 with an example of a condition management computingapparatus 12 is illustrated in FIGS. 1-2. In this particular example,the environment 10 includes the condition management computing apparatus12, imaging devices 14(1)-14(n), and a property records storage serverdevice 16 coupled via one or more communication networks 18, althoughthe environment could include other types and numbers of systems,devices, components, and/or other elements as is generally known in theart and will not be illustrated or described herein. This technologyprovides a number of advantages including providing methods,non-transitory computer readable medium, and apparatuses for moreaccurately and effectively assessing prior property conditioning of atotal loss vehicle or other property.

Referring more specifically to FIGS. 1-2, the condition managementcomputing apparatus 12 is programmed to assess prior propertyconditioning of a total loss vehicle or other property as illustratedand described herein, although the apparatus can perform other typesand/or numbers of functions or other operations and this technology canbe utilized with other types of claims. In this particular example, thecondition management computing apparatus 12 includes a processor 24, amemory 26, and a communication interface 28 which are coupled togetherby a bus 30, although the condition management computing apparatus 12may include other types and/or numbers of physical and/or virtualsystems, devices, components, and/or other elements in otherconfigurations.

The processor 24 in the condition management computing apparatus 12 mayexecute one or more programmed instructions stored in the memory 26 forassessing prior property conditioning of a total loss vehicle or otherproperty as illustrated and described in the examples herein, althoughother types and numbers of functions and/or other operation can beperformed. The processor 24 in the condition management computingapparatus 12 may include one or more central processing units and/orgeneral purpose processors with one or more processing cores, forexample.

The memory 26 in the condition management computing apparatus 12 storesthe programmed instructions and/or other data for one or more aspects ofthe present technology as described and illustrated herein, althoughsome or all of the programmed instructions and/or data could be storedand/or executed or obtained elsewhere. A variety of different types ofmemory storage devices, such as a random access memory (RAM) or a readonly memory (ROM) in the system or a floppy disk, hard disk, CD ROM, DVDROM, or other computer readable medium which is read from and written toby a magnetic, optical, or other reading and writing system that iscoupled to the processor 24, can be used for the memory 26. In thisparticular example, the memory 26 includes condition assessmentArtificial Intelligence (AI) 32 and a condition guideline rating datastore 34, although the memory 26 can comprise other types and/or numbersof other modules, programmed instructions and/or data. Examples of theprogrammed instructions and/or data in the condition assessmentArtificial Intelligence (AI) 32 and the condition guideline rating datastore 34 are illustrated and described by way of the examples herein.

The communication interface 28 in the condition management computingapparatus 12 operatively couples and communicates between one or more ofthe imaging devices 14(1)-14(n) and the property records storage serverdevice 16, which are all coupled together by one or more of thecommunication networks 18, although other types and numbers ofcommunication networks or systems with other types and numbers ofconnections and configurations to other devices and elements. By way ofexample only, the communication networks 18 can use TCP/IP over Ethernetand industry-standard protocols, including NFS, CIFS, SOAP, XML, LDAP,SCSI, and SNMP, although other types and numbers of communicationnetworks, can be used. The communication networks 18 in this example mayemploy any suitable interface mechanisms and network communicationtechnologies, including, for example, any local area network, any widearea network (e.g., Internet), teletraffic in any suitable form (e.g.,voice, modem, and the like), Public Switched Telephone Network (PSTNs),Ethernet-based Packet Data Networks (PDNs), and any combinations thereofand the like.

In this particular example, each of the imaging devices 14(1)-14(n) maycapture and provide images, such as picture and/or videos by way ofexample only, of parts and/or all of an interior, exterior, mechanicalelements, and/or other categories of the total loss vehicle or otherproperty for an assessment of prior property conditioning by thecondition management computing apparatus 12, although the images can beobtained by the condition management computing apparatus 12 in othermanners and/or from other sources.

The property records storage server device 16 may store and providerequested information and/or other content about the total loss vehicleor other property, such as which part or parts and/or categories need tobe examined for assessing prior property conditioning as well as otherinformation, such as vehicle information and/or owner data andidentification by way of example only. The condition managementcomputing apparatus 12 may interact with the property records storageserver device 16 via one or more of the communication networks 18, forexample, although other types and/or numbers of storage media in otherconfigurations with other stored information could be used. The propertyrecords storage server device 16 also may comprise various combinationsand types of storage hardware and/or software and represent a systemwith multiple network server devices in a data storage pool, which mayinclude internal or external networks. Various network processingapplications, such as CIFS applications, NFS applications, HTTP WebNetwork server device applications, and/or FTP applications, may beoperating on the property records storage server device 16 and maytransmit data in response to requests from the condition managementcomputing apparatus 12.

Each of the imaging devices 14(1)-14(n) and the property records storageserver device 16 may include a processor, a memory, and a communicationinterface, which are coupled together by a bus or other link, althoughother type and/or numbers of other devices and/or nodes as well as othernetwork elements could be used.

Although the exemplary network environment 10 with the conditionmanagement computing apparatus 12, the imaging devices 14(1)-14(n), theproperty records storage server device 16, and the communicationnetworks 18 are described and illustrated herein, other types andnumbers of systems, devices, components, and/or elements in othertopologies can be used. It is to be understood that the systems of theexamples described herein are for exemplary purposes, as many variationsof the specific hardware and software used to implement the examples arepossible, as will be appreciated by those skilled in the relevantart(s).

In addition, two or more computing systems or devices can be substitutedfor any one of the systems or devices in any example. Accordingly,principles and advantages of distributed processing, such as redundancyand replication also can be implemented, as desired, to increase therobustness and performance of the devices, apparatuses, and systems ofthe examples. The examples may also be implemented on computer system(s)that extend across any suitable network using any suitable interfacemechanisms and traffic technologies, including by way of example onlyteletraffic in any suitable form (e.g., voice and modem), wirelesstraffic media, wireless traffic networks, cellular traffic networks, G3traffic networks, Public Switched Telephone Network (PSTNs), Packet DataNetworks (PDNs), the Internet, intranets, and combinations thereof.

The examples also may be embodied as a non-transitory computer readablemedium having instructions stored thereon for one or more aspects of thepresent technology as described and illustrated by way of the examplesherein, as described herein, which when executed by the processor, causethe processor to carry out the steps necessary to implement the methodsof this technology as described and illustrated with the examplesherein.

An example of a method of assessing prior property conditioning of atotal loss vehicle or other property will now be described withreference to FIGS. 1-9. Referring more specifically to FIG. 3, in step300 the condition management computing apparatus 12 may interact withthe property records storage server 16 to identify the total lossvehicle or other property and the one or more parts that need to beassessed for conditioning, although the identity of the total lossvehicle or other property and/or of the one or more parts that need tobe assessed can be obtained in other manners. By way of example only,the identified parts to assess for conditioning of the total lossvehicle or other property may be determined by the condition managementcomputing apparatus 12 based on interactions with the property recordsstorage server 16 to comprise categories of interior, exterior,operational mechanical elements, and tires, although other types and/ornumbers of categories may be used. In this example, the interiorcategory of the total loss vehicle may include the carpet, glass,dash/console, trim, and seats as shown in FIG. 4, the exterior categoryof the total loss vehicle may include the right front, right side, rightrear, rear, left rear, left side, left front, and front of the totalloss vehicle as shown in FIG. 5, the operational mechanical elementscategory of the total loss vehicle may include the engine compartment asshown in FIG. 6, a braking system, and a steering system, and the tirecategory of the total loss vehicle may include each of tires of thetotal loss vehicle, although other types and/or numbers of categoriesand elements in each category may be used.

The condition management computing apparatus 12 may also optionallyreceive or otherwise obtain other data or information relating to theproperty to be assessed for property conditioning and/or for appraisalof the total loss, such as owner data and other claim processing relateddata. This example of the technology is able to more accurately andconsistently identify the particular one or more categories and the oneor more parts in each total loss vehicle or other property to assess forconditioning. Additionally, this example of the technology allows forthe easy adjustment of and then ongoing consistent application of theparticular categories and/or parts which previously was not available.

In step 302, the condition management computing apparatus 12 may obtainone or more electronic images of each of the identified one or moreparts of the total loss vehicle or other property. By way of exampleonly, one or more of the imaging devices 14(1)-14(n) may be used tocapture images, such as pictures and/or videos, of the identified partsin each of the categories for the assessment of the part conditioningthat is used to obtain the property conditioning of each of thecategories and of the total loss vehicle or other property. In theexamples discussed herein, electronic image refers to an image or videoin a format compatible for automated analysis. By way of example only,an electronic image of one tire is shown in FIG. 7 and two images ofparts of an interior and an exterior of a total loss vehicle are shownin FIG. 8.

This example of the technology is able to ensure that the necessaryimages for assessment of conditioning of each of the parts in each ofthe categories is obtained to ensure a more accurate and consistentautomated property conditioning assessment. If any necessary images forany of the categories is missing, the condition management computingapparatus 12 may identify and obtain the other one or more missingelectronic images, such as by determining based on a stored table forthe associated type of loss property on what images are need and thentransmitting an electronic request for any missing image or images.Although in this example, the images are obtained from one or more ofthe imaging devices 14(1)-14(n), the images can be obtained by thecondition management computing apparatus 12 from other sources, such asfrom prior stored images of the total loss vehicle or other property,e.g. a recent inspection of the total loss vehicle or other property.

In step 304, the condition management computing apparatus 12 may analyzethe one or more electronic images for each of the one or more partsusing the condition assessment artificial intelligence (AI) 32 and thecondition guidelines rating data 34 to obtain an assessment of partconditioning comprising a rating for each of the one or more parts ofthe total loss vehicle, such as parts of the internal and externalsections of the vehicle by way of example only. The condition assessmentartificial intelligence (AI) 32 is a technological improvement overprior software assessment technologies and enables accurate andefficient condition assessments of the parts of the total loss vehicleor other property without requiring any disassembly. The conditionassessment artificial intelligence (AI) 32 may utilize by way of exampledeep learning through an analysis of prior images and conditionassessments of a total loss vehicle of other property to generate storedconditioning data and a conditioning assessment algorithm or otherexecutable rule or rules which can be used for new conditioningassessments. Additionally, the condition assessment artificialintelligence (AI) 32 may also continue to learn and update based on eachnew assessment and any received feedback to further refine the aconditioning assessment algorithm or other executable rule or rules.Further, the condition assessment artificial intelligence (AI) 32 may betrained to identify and distinguish between routine or expected wearand/or surface grime and actual damage or deterioration of for each typeof a plurality of vehicles or other property to further refine the aconditioning assessment algorithm or other executable rule or rules.Even further, the condition assessment artificial intelligence (AI) 32may be trained to provide an assessment of parts which are not visiblewithout requiring disassembly of the vehicle or other item beinganalyzed based on the obtained images and data on the vehicle or otheritem being assessed to further refine the a conditioning assessmentalgorithm or other executable rule or rules.

By way of example only, the condition guidelines rating data 34 for eachof a plurality of types of vehicles or other property may compriseinformation to set condition ratings for each of the parts in each ofthe categories. The condition management computing apparatus 12 mayidentify for example based on an identification of the total lossvehicle or other property the appropriate condition guidelines ratingdata for the particular total loss vehicle or other property beingassessed for conditioning, although other manners for identifying one ofthe guidelines rating data may be used, such as from an analysis ofelectronic images by way of example. By way of example only, automatedratings obtained for each of the parts in each of the categories in step304 is illustrated in FIG. 9.

In step 306, the condition management computing apparatus 12 maydetermine a prior property conditioning of the total loss property basedon the automated analysis of the one or more obtained electronic images.In this example, the condition management computing apparatus 12 mayalso obtain a part weighting factor for each of the one or more parts ineach of the categories of the total loss vehicle or other property. Aweighting factor may be assigned to each of the parts in each of thecategories by the condition management computing apparatus 12 as shownby way of example in FIG. 9. By way of example, the weighting factorswhich may be utilized by the condition management computing apparatus 12may vary based on the particular type of vehicle or other property andmay be based on one or more of these considerations: (1)Importance/Rank—This refers to the significance the subcategory has tothe overall condition of the vehicle, meaning from a consumersperspective; (2) Occurrence Rate—This refers to the frequency that thesubcategory component(s) is subjected to Wear/Tear/Damage. (3)Value—This refers to the value or cost that the subcategory component(s)has on the overall vehicles value compared to other subcategorycomponent(s).

The condition management computing apparatus 12 may determine a priorpart conditioning assessment for each part in each category based on theanalysis in step 304 and then may apply the weighting factor based onthe particular type of vehicle or other property to each of the ratingsfor each of the parts. Accordingly, a weighted condition assessmentrating for the prior property conditioning for each of the parts, thecategories, and/or the overall vehicle or other property may be obtainedas shown in FIG. 9.

In step 308, the condition management computing apparatus 12 may providethe determined rating for the prior property conditioning of the totalloss property, such as for an electronic claim for the total lossvehicle or other property that is being processed, although theassessment can be provide for other purposes.

In step 310, the condition management computing apparatus may determinea loss appraisal of the property based on an obtained current marketvalue of the property obtained from another source and then adjusted bysubtracting the determined prior property conditioning of the total lossproperty or making some other programmed adjustment.

Accordingly, as illustrated and described by way of the examples herein,this technology provides more accurate and effective assessment of priorconditioning of a total loss vehicle or other property which is notroutine or conventional in this technology area. With this technology,condition assessment artificial intelligence (AI) may be used toautomatically analyze one or more electronic images and/or videos inconjunction with pre-established condition guidelines rating data toautomatically assess a prior property condition of a total loss vehicleor other property. This technology with the assessment AI providestechnological improvements resulting in increases in accuracy andconsistency when evaluating a prior conditioning of the vehicle or otherproperty. Further, with these improvements in the automated evaluationprocess there is an increase in settlement efficiency of electronicinsurance claims relating to a total loss vehicle or other property byeliminating the need for an onsite inspection of the vehicle or otherproperty.

Having thus described the basic concept of the invention, it will berather apparent to those skilled in the art that the foregoing detaileddisclosure is intended to be presented by way of example only, and isnot limiting. Various alterations, improvements, and modifications willoccur and are intended to those skilled in the art, though not expresslystated herein. These alterations, improvements, and modifications areintended to be suggested hereby, and are within the spirit and scope ofthe invention. Additionally, the recited order of processing elements orsequences, or the use of numbers, letters, or other designationstherefore, is not intended to limit the claimed processes to any orderexcept as may be specified in the claims. Accordingly, the invention islimited only by the following claims and equivalents thereto.

What is claimed is:
 1. A method comprising: automatically analyzing, bythe computing apparatus, one or more electronic images of a total lossproperty based on one or more prior condition assessments and conditionguidelines rating data associated with the total loss property;determining, by the computing apparatus, a prior property conditioningof the total loss property based on the analysis of the one or moreobtained images; and providing, by the computing apparatus, thedetermined prior property conditioning of the total loss property. 2.The method as set forth in claim 1 wherein the analyzing the one or moreelectronic images further comprises analyzing, by the computingapparatus, the one or more images with condition assessment artificialintelligence based on stored conditioning data encoded from one or moreprior condition assessments and the condition guidelines rating data. 3.The method as set forth in claim 2 further comprising: identifying, bythe condition management computing apparatus, one or more parts of thetotal loss property based on an identification of the total lossproperty; wherein the one or more electronic images further comprise oneor more electronic images of each of the identified one or more parts ofthe total loss property.
 4. The method as set forth in claim 3 whereinthe analyzing the one or more electronic images and the determining theprior property conditioning of the total loss property furthercomprises: analyzing, by the computing apparatus, the one or moreelectronic images for each of the one or more parts using the conditionassessment artificial intelligence and the condition guidelines ratingdata for each of the one or more parts of the total loss property; anddetermining, by the computing apparatus, a prior part conditioning foreach of the one or more parts of the total loss property based on theanalysis of the one or more electronic images for each of the one ormore parts of the total loss property and the prior propertyconditioning of the total loss property based on the prior partconditioning for each of the one or more parts of the total lossproperty.
 5. The method as set forth in claim 4 further comprising:obtaining, by the computing apparatus, a part weighting factor for eachof the one or more parts of the property; wherein the determining theprior property conditioning of the total loss property based on theprior part conditioning for each of the one or more parts of the totalloss property is further based on applying the part weighting factor foreach of the one or more parts of the property on the prior partconditioning for each of the one or more parts of the total lossproperty.
 6. The method as set forth in claim 5 further comprising:obtaining, by the computing apparatus, two or more categories of the oneor more parts of the property, each of the two or more categories havinga category weighting factor, wherein the part weighting factor for eachof the one or more parts of the property in each category is based onthe category weighting factor.
 7. The method as set forth in claim 1further comprising determining, by the computing apparatus, a lossappraisal of the property based on an obtained current market value ofthe property adjusted by the determined prior property conditioning ofthe total loss property.
 8. A non-transitory computer readable mediumhaving stored thereon instructions for automated assessment ofconditioning comprising executable code which when executed by one ormore processors, causes the one or more processors to: automaticallyanalyze one or more electronic images of a total loss property based onone or more prior condition assessments and condition guidelines ratingdata associated with the total loss property; determine a prior propertyconditioning of the total loss property based on the analysis of the oneor more obtained images; and provide the determined prior propertyconditioning of the total loss property.
 9. The medium as set forth inclaim 8 wherein for the analyze the one or more electronic images, theexecutable code when executed by the one or more processors furthercauses the one or more processors to: analyze the one or more imageswith condition assessment artificial intelligence based on storedconditioning data encoded from one or more prior condition assessmentsand the condition guidelines rating data.
 10. The medium as set forth inclaim 9 wherein the executable code when executed by the one or moreprocessors further causes the one or more processors to: identify one ormore parts of the total loss property based on an identification of thetotal loss property; wherein the one or more electronic images furthercomprises one or more electronic images of each of the identified one ormore parts of the total loss property.
 11. The medium as set forth inclaim 10 wherein the executable code when executed by the one or moreprocessors for the analyze the one or more electronic images and thedetermine the prior property conditioning of the total loss propertyfurther causes the one or more processors to: analyze the one or moreelectronic images for each of the one or more parts using the conditionassessment artificial intelligence and the condition guidelines ratingdata for each of the one or more parts of the total loss property; anddetermine a prior part conditioning for each of the one or more parts ofthe total loss property based on the analysis of the one or moreelectronic images for each of the one or more parts of the total lossproperty and the prior property conditioning of the total loss propertybased on the prior part conditioning for each of the one or more partsof the total loss property.
 12. The medium as set forth in claim 11wherein the executable code when executed by the one or more processorsfurther causes the one or more processors to: obtain a part weightingfactor for each of the one or of the parts of the property; wherein thedetermine the prior property conditioning of the total loss propertybased on the prior part conditioning for each of the one or more partsof the total loss property is further based on applying the partweighting factor for each of the one or more parts of the property onthe prior part conditioning for each of the one or more parts of thetotal loss property.
 13. The medium as set forth in claim 12 wherein theexecutable code when executed by the one or more processors furthercauses the one or more processors to: obtain two or more categories ofthe one or more parts of the property, each of the two or morecategories having a category weighting factor, wherein the partweighting factor for each of the one or more parts of the property ineach category is based on the category weighting factor.
 14. The mediumas set forth in claim 8 wherein the executable code when executed by theone or more processors further causes the one or more processors to:determine a loss appraisal of the property based on an obtained currentmarket value of the property adjusted by the determined prior propertyconditioning of the total loss property.
 15. A computing apparatuscomprising: a processor; and a memory coupled to the processor which isconfigured to be capable of executing programmed instructions stored inthe memory to: automatically analyze one or more electronic images of atotal loss property based on one or more prior condition assessments andcondition guidelines rating data associated with the total lossproperty; determine a prior property conditioning of the total lossproperty based on the analysis of the one or more obtained images; andprovide the determined prior property conditioning of the total lossproperty.
 16. The apparatus as set forth in claim 15 wherein for theanalyze the one or more electronic images, the processor coupled to thememory is further configured to be capable of executing at least oneadditional programmed instruction stored in the memory to: analyze theone or more images with condition assessment artificial intelligencebased on stored conditioning data encoded from one or more priorcondition assessments and the condition guidelines rating data.
 17. Theapparatus as set forth in claim 16 wherein the processor coupled to thememory is further configured to be capable of executing at least onadditional programmed instruction stored in the memory to: identify oneor more parts of the total loss property based on an identification ofthe total loss property; wherein the one or more electronic imagesfurther comprises one or more electronic images of each of theidentified one or more parts of the total loss property.
 18. Theapparatus as set forth in claim 17 wherein the processor coupled to thememory is further configured for the analyze the one or more electronicimages and the determine the prior property conditioning of the totalloss property to be capable of executing at least one additionalprogrammed instruction stored in the memory to: analyze the one or moreelectronic images for each of the one or more parts using the conditionassessment artificial intelligence and the condition guidelines ratingdata for each of the one or more parts of the total loss property; anddetermine a prior part conditioning for each of the one or more parts ofthe total loss property based on the analysis of the one or moreelectronic images for each of the one or more parts of the total lossproperty and the prior property conditioning of the total loss propertybased on the prior part conditioning for each of the one or more partsof the total loss property.
 19. The apparatus as set forth in claim 18wherein the processor coupled to the memory is further configured to becapable of executing at least one additional programmed instructionstored in the memory to: obtain a part weighting factor for each of theone or more parts of the property; wherein the determine the priorproperty conditioning of the total loss property based on the prior partconditioning for each of the one or more parts of the total lossproperty is further based on applying the part weighting factor for eachof the one or more parts of the property on the prior part conditioningfor each of the one or more parts of the total loss property.
 20. Theapparatus as set forth in claim 19 wherein the processor coupled to thememory is further configured to be capable of executing at least oneadditional programmed instruction stored in the memory to: obtain two ormore categories of the one or more parts of the property, each of thetwo or more categories having a category weighting factor, wherein thepart weighting factor for each of the one or more parts of the propertyin each category is based on the category weighting factor.
 21. Theapparatus as set forth in claim 15 wherein the processor coupled to thememory is further configured to be capable of executing at least oneadditional programmed instruction stored in the memory to: determine aloss appraisal of the property based on an obtained current market valueof the property adjusted by the determined prior property conditioningof the total loss property.